Leveraging artificial intelligence in ischemic stroke imaging

Shafaat, Omid; Bernstock, Joshua D.; Shafaat, Amir; Yedavalli, Vivek; Elsayed, Galal; Gupta, Saksham; Sotoudeh, Ehsan; Sair, Haris I.; Yousem, David M.; Sotoudeh, Houman

doi:10.1016/j.neurad.2021.05.001

Cited by 33 publications

(21 citation statements)

References 67 publications

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“…Over the past decades, Artificial Intelligence, and more particularly Machine Learning (ML), have been increasingly used for various medical imaging tasks [31]. In this paper, the bifurcation labeling process is considered as a multi-label classification problem.…”

Section: Machine Learning Classificationmentioning

confidence: 99%

Automatic Classification of the Cerebral Vascular Bifurcations Using Dimensionality Reduction and Machine Learning

et al. 2022

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Section: Machine Learning Classificationmentioning

confidence: 99%

Automatic Classification of the Cerebral Vascular Bifurcations Using Dimensionality Reduction and Machine Learning

et al. 2022

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“…Detection of ischemic stroke in the early hyperacute (0-6 hours) and late hyperacute stage (6-24 hours) is critical in the selection of patients for intravenous or mechanical thrombectomy [9]. Predicting the outcome of the intervention, including accurate identification of the Alberta Stroke Program Early CT Score (ASPECTS), penumbra tissue, successful revascularization, and risk of hemorrhagic transformation after treatment, are crucial in the long-term outcome and prognosis [10,11].…”

Section: Ischemic Strokementioning

confidence: 99%

Emerging Applications of Radiomics in Neurological Disorders: A Review

Sotoudeh¹,

Sarrami²,

Roberson³

et al. 2021

Cureus

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Radiomics has achieved significant momentum in radiology research and can reveal image information invisible to radiologists' eyes. Radiomics first evolved for oncologic imaging. Oncologic applications (histopathology, tumor grading, gene mutation analysis, patient survival, and treatment response prediction) of radiomics are widespread. However, it is not limited to oncologic analysis, and any digital medical images can benefit from radiomics analysis. This article reviews the current literature on radiomics in non-oncologic, neurological disorders including ischemic strokes, hemorrhagic stroke, cerebral aneurysms, and demyelinating disorders.

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“…Time of diagnosis is crucial for time-related treatments, which allow the improvement of clinical outcomes and the reduction of disability, according to the adage, “time is brain” [ 3 ]. In clinical practice, early identification of the mechanism of acute ischemic stroke is highly dependent on a reliable imaging examination that must be interpreted promptly [ 4 ]. Imaging techniques are the cornerstone of the work-up in stroke patients.…”

Section: Introductionmentioning

confidence: 99%

“…It represents a new technology able to analyze complex data using automated algorithms for obtaining a final output. IS is one of the medical fields that have been extensively affected by the AI revolution [ 4 ]. In fact, AI algorithms have been shown to be able to perform accurate lesion classification, detection, and segmentation in brain tissue.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

et al. 2023

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The correct recognition of the etiology of ischemic stroke (IS) allows tempestive interventions in therapy with the aim of treating the cause and preventing a new cerebral ischemic event. Nevertheless, the identification of the cause is often challenging and is based on clinical features and data obtained by imaging techniques and other diagnostic exams. TOAST classification system describes the different etiologies of ischemic stroke and includes five subtypes: LAAS (large-artery atherosclerosis), CEI (cardio embolism), SVD (small vessel disease), ODE (stroke of other determined etiology), and UDE (stroke of undetermined etiology). AI models, providing computational methodologies for quantitative and objective evaluations, seem to increase the sensitivity of main IS causes, such as tomographic diagnosis of carotid stenosis, electrocardiographic recognition of atrial fibrillation, and identification of small vessel disease in magnetic resonance images. The aim of this review is to provide overall knowledge about the most effective AI models used in the differential diagnosis of ischemic stroke etiology according to the TOAST classification. According to our results, AI has proven to be a useful tool for identifying predictive factors capable of subtyping acute stroke patients in large heterogeneous populations and, in particular, clarifying the etiology of UDE IS especially detecting cardioembolic sources.

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Leveraging artificial intelligence in ischemic stroke imaging

Cited by 33 publications

References 67 publications

Automatic Classification of the Cerebral Vascular Bifurcations Using Dimensionality Reduction and Machine Learning

Automatic Classification of the Cerebral Vascular Bifurcations Using Dimensionality Reduction and Machine Learning

Emerging Applications of Radiomics in Neurological Disorders: A Review

Artificial Intelligence in Acute Ischemic Stroke Subtypes According to Toast Classification: A Comprehensive Narrative Review

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